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Creating a custom model in Tensor Art involves a streamlined process from designing a detailed prompt to publishing a fully trained model. This concise guide outlines the key steps to create and refine your model efficiently.1. Crafting the PromptStart with a precise, descriptive prompt to define the desired output. Example:A hyperrealistic Christmas tree designed for the Flux checkpoint. This vibrant evergreen glimmers with frosted needles, adorned with glowing glass baubles in gold, emerald, and crimson. Hand-carved figurines, radiant crystal icicles, and flowing golden ribbons add texture. Warm white fairy lights cascade through the branches, and the gem-encrusted star topper emits a radiant aura. A velvet-like tree skirt surrounds ornate gift boxes at the base, with swirling mist enhancing depth against frosted windows reflecting faint twinkling lights.A detailed prompt ensures accurate and high-quality outputs.2. Selecting ParametersOptimize settings for the best results:Checkpoint: FluxSampling Method: Euler Ancestral (Euler a)CFG Scale: 8Steps: 50Resolution: 1024x1024Denoising Strength: 0.55VAE: vae-ft-mse-840000Seed: Random3. Generating Base ImagesGenerate multiple outputs using the prompt.Review and refine until achieving desired results.Save high-quality images for training.4. Preparing for Model Traininga. Dataset CreationSelect 10-15 high-quality, thematic images.Standardize image resolution to 512x512.Annotate images with relevant metadata.b. PreprocessingNormalize brightness and contrast.Augment images with flips and rotations for diversity.5. Training the Modela. Training ConfigurationChoose between fine-tuning or full-model training based on complexity.Recommended settings:Epochs: 8-10Batch Size: 2-4Learning Rate: 0.0001b. Training StepsUpload the preprocessed dataset to Tensor Art.Configure training settings.Start training and monitor logs.Review sample outputs for quality.6. Refining and ValidatingGenerate test images with the trained model.Assess results and adjust weights as needed.7. Publishing the Modela. Final AdjustmentsAssign a descriptive name (e.g., "Hyperrealistic Christmas Tree Model").Include key details: prompt, parameters, and example outputs.b. Sharing the ModelExport and upload the model to Tensor Art’s gallery or a private repository.Provide sample images to showcase capabilities.The outcome:Link:https://tensor.art/models/806563234615312507?source_id=nje1r1HmlkawovMoYHzy8hknFeel free to use this model in your art. Thank you in advanced.😁ConclusionBy following these steps, you can efficiently design, train, and publish a custom Tensor Art model. This streamlined workflow ensures exceptional AI-generated imagery that brings your creative vision to life.#christmas walkthrough

Creating an AI Workflow for Image-to-Video Conversion AI TOOL: A Breakdown of 10 Nodes In this article, we will explore a workflow designed in Tensor Art for converting images into dynamic video sequences. The process involves ten interconnected nodes, each serving a distinct purpose in the pipeline. Let’s break down the workflow step-by-step, starting with the Load Image node and ending with the Video Combine node.Image-to-Video Workflow in Tensor ArtThe diagram below illustrates the complete workflow with labeled nodes:Node ExplanationsLoad ImageThis is the entry point of the workflow, where the image file is uploaded to be used as the base for video generation.Resize ImageResizes the uploaded image to match the desired dimensions (e.g., 720x480 pixels). This ensures the output video maintains consistent proportions and compatibility with subsequent processing steps.Load CLIPLoads a CLIP model for text-to-image or text-to-video encoding. This allows the workflow to interpret text-based prompts, influencing how the visual content evolves during video generation.CogVideo TextEncode (Prompt)Encodes the primary textual prompt (e.g., describing what the flames or animations should look like) into a format understandable by the video model.CogVideo TextEncode (Secondary)Encodes an additional textual prompt to modify or enhance the animation, such as describing changes in intensity or movement within the scene.Download CogVideo ModelDownloads and initializes the CogVideo model, which is the primary AI tool responsible for generating video frames from textual and visual inputs.CogVideo ImageEncodeConverts the resized image into a format compatible with the CogVideo system, acting as a bridge between the static image and the dynamic video generation.CogVideo SamplerThe core of the workflow, this node generates video frames based on the encoded prompts, image data, and model configuration. Parameters such as the number of frames, steps, and noise strength are set here to control the quality and length of the video.CogVideo DecodeDecodes the sampled video data into a usable video format. This includes applying settings for tile sizing, overlap, and resolution to ensure the final video meets the desired specifications.Video CombineCombines the generated video frames into a cohesive video file. This node allows additional settings such as frame rate, loop count, and output format (e.g., MP4).Summary of WorkflowThis workflow efficiently converts an image into a video through a seamless integration of ten nodes. Starting from image uploading (Load Image) and resizing (Resize Image), the process uses text prompts (CLIP and CogVideo TextEncode) and AI models (CogVideo Sampler and Decode) to generate dynamic animations. The final product is rendered and saved through the Video Combine node. This modular approach ensures flexibility and precision in creating high-quality AI-generated videos. You can use this workflow to create AI TOOL for image to video!